Automotive · document intelligence
Digitising documents and generating compliance reports with OCR and ML
Used UiPath document understanding to extract invoice and customs-form data, validate it against business rules, and generate structured pricing reports.
Delivered engagementThe challenge
Pricing and customs reporting depended on information spread across invoices, customs forms, part lists, bills of material, and mapping workbooks. Source documents arrived in PDF and Excel formats, and the reporting rules had to reconcile part numbers, country groupings, exchange rates, tariff schemes, duty exemptions, and duplicate or corrected records.
Document variation made manual rekeying slow and error-prone, while the complexity of the calculations made a purely visual desktop macro insufficient. The process needed both machine-learning extraction and deterministic business rules.
What Monic delivered
Monic delivered an unattended UiPath document-intelligence workflow using OCR and a machine-learning skill to classify source pages and extract invoice and customs data into a structured workbook. Validation results and unprocessed pages remain visible for operator review rather than being silently accepted.
After review, a second package applies the maintained mapping and bill-of-material rules, selects the qualifying pricing records, calculates freight, insurance, exchange-rate and duty values, and generates model-specific reports and summaries. Operators can request both 12-month and six-month comparison periods. Relevant source pages are renamed and archived with duplicate handling for later traceability.
The result
The engagement established a repeatable workflow that connects document digitisation, human validation, rule-based pricing analysis, source-document archiving, and report generation. Recoverable handling for locked files, incorrect worksheet names, unmapped values, and failed document classification gives operators a clear route to correct and rerun a case.
What this case demonstrates
The engineering lessons behind the result.
These are the delivery patterns a team facing a similar operating problem should plan for.
Extraction and calculation are different problems
OCR and machine learning convert varied documents into structured fields; deterministic rules then perform the governed comparisons and calculations. Keeping those responsibilities separate makes each easier to test.
Human review belongs between uncertain and governed steps
Operators validate extracted data before the reporting package applies financial rules, preventing uncertain document interpretation from flowing directly into a compliance output.
Operator constraints must be designed
Health checks, concurrency rules, visible failures, duplicate handling, correction paths, and rerun guidance are part of a production unattended automation—not merely user-guide detail.